OpenRaiser/Intern-Atlas huggingface dataset:
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
Venue: arXiv cs.CV 2019
Year: 2019

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25-30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Venue: arXiv cs.CV 2019
Year: 2019

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In addition, we cast the decoding process as a binary classification of the point cloud occupancy map. Our method outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with 51.5% BDBR savings on average. Moreover, while octree-based methods face exponential diminution of the number of points at low bitrates, our method still produces high resolution outputs even at low bitrates. Code and supplementary material are available at https://github.com/mauriceqch/pcc_geo_cnn .
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Venue: arXiv cs.IT 2019
Year: 2019

Data compression has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where data compression is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where data compression is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and resource allocation. We show that the proposed optimal algorithm for data compression at only the mobile users can reduce the WEDC by a few hundred percent compared to computation offloading strategies that do not leverage data compression or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional data compression at the fog server can further reduce the WEDC.
Reachability in Vector Addition Systems is Primitive-Recursive in Fixed Dimension
Venue: arXiv cs.LO 2019
Year: 2019

The reachability problem in vector addition systems is a central question, not only for the static verification of these systems, but also for many inter-reducible decision problems occurring in various fields. The currently best known upper bound on this problem is not primitive-recursive, even when considering systems of fixed dimension. We provide significant refinements to the classical decomposition algorithm of Mayr, Kosaraju, and Lambert and to its termination proof, which yield an ACKERMANN upper bound in the general case, and primitive-recursive upper bounds in fixed dimension. While this does not match the currently best known TOWER lower bound for reachability, it is optimal for related problems.
EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables
Venue: arXiv cs.HC 2019
Year: 2019

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users' lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.
Cross-study Reliability of the Open Card Sorting Method
Venue: arXiv cs.HC 2019
Year: 2019

Information architecture forms the foundation of users' navigation experience. Open card sorting is a widely-used method to create information architectures based on users' groupings of the content. However, little is known about the method's cross-study reliability: Does it produce consistent content groupings for similar profile participants involved in different card sort studies? This paper presents an empirical evaluation of the method's cross-study reliability. Six card sorts involving 140 participants were conducted: three open sorts for a travel website, and three for an eshop. Results showed that participants provided highly similar card sorting data for the same content. A rather high agreement of the produced navigation schemes was also found. These findings provide support for the cross-study reliability of the open card sorting method.
A 68 uW 31 kS/s Fully-Capacitive Noise-Shaping SAR ADC with 102 dB SNDR
Venue: arXiv cs.AR 2019
Year: 2019

This paper presents a 17 bit analogue-to-digital converter that incorporates mismatch and quantisation noise-shaping techniques into an energy-saving 10 bit successive approximation quantiser to increase the dynamic range by another 42 dB. We propose a novel fully-capacitive topology which allows for high-speed asynchronous conversion together with a background calibration scheme to reduce the oversampling requirement by 10x compared to prior-art. A 0.18 um CMOS technology is used to demonstrate preliminary simulation results together with analytic measures that optimise parameter and topology selection. The proposed system is able to achieve a FoMS of 183 dB for a maximum signal bandwidth of 15.6 kHz while dissipating 68 uW from a 1.8 V supply. A peak SNDR of 102 dB is demonstrated for this rate with a 0.201 mm^2 area requirement.
CRAFT: A multifunction online platform for speech prosody visualisation
Venue: arXiv cs.SD 2019
Year: 2019

There are many research tools which are also used for teaching the acoustic phonetics of speech rhythm and speech melody. But they were not purpose-designed for teaching-learning situations, and some have a steep learning curve. CRAFT (Creation and Recovery of Amplitude and Frequency Tracks) is custom-designed as a novel flexible online tool for visualisation and critical comparison of functions and transforms, with implementations of the Reaper, RAPT, PyRapt, YAAPT, YIN and PySWIPE F0 estimators, three Praat configurations, and two purpose-built estimators, PyAMDF, S0FT. Visualisations of amplitude and frequency envelope spectra, spectral edge detection of rhythm zones, and a parametrised spectrogram are included. A selection of audio clips from tone and intonation languages is provided for demonstration purposes. The main advantages of online tools are consistency (users have the same version and the same data selection), interoperability over different platforms, and ease of maintenance. The code is available on GitHub.
Distributed Vector Representations of Folksong Motifs
Venue: arXiv cs.IR 2019
Year: 2019

This article presents a distributed vector representation model for learning folksong motifs. A skip-gram version of word2vec with negative sampling is used to represent high quality embeddings. Motifs from the Essen Folksong collection are compared based on their cosine similarity. A new evaluation method for testing the quality of the embeddings based on a melodic similarity task is presented to show how the vector space can represent complex contextual features, and how it can be utilized for the study of folksong variation.
RAIRE: Risk-Limiting Audits for IRV Elections
Venue: arXiv cs.DS 2019
Year: 2019

Risk-limiting post election audits guarantee a high probability of correcting incorrect election results, independent of why the result was incorrect. Ballot-polling audits select ballots at random and interpret those ballots as evidence for and against the reported result, continuing this process until either they support the recorded result, or they fall back to a full manual recount. For elections with digitised scanning and counting of ballots, a comparison audit compares randomly selected digital ballots with their paper versions. Discrepancies are referred to as errors, and are used to build evidence against or in support of the recorded result. Risk-limiting audits for first-past-the-post elections are well understood, and used in some US elections. We define a number of approaches to ballot-polling and comparison risk-limiting audits for Instant Runoff Voting (IRV) elections. We show that for almost all real elections we found, we can perform a risk-limiting audit by looking at only a small fraction of the total ballots (assuming no errors were made in the tallying and distribution of votes).
Bidirectional Recurrent Models for Offensive Tweet Classification
Venue: arXiv cs.CL 2019
Year: 2019

In this paper we propose four deep recurrent architectures to tackle the task of offensive tweet detection as well as further classification into targeting and subject of said targeting. Our architectures are based on LSTMs and GRUs, we present a simple bidirectional LSTM as a baseline system and then further increase the complexity of the models by adding convolutional layers and implementing a split-process-merge architecture with LSTM and GRU as processors. Multiple pre-processing techniques were also investigated. The validation F1-score results from each model are presented for the three subtasks as well as the final F1-score performance on the private competition test set. It was found that model complexity did not necessarily yield better results. Our best-performing model was also the simplest, a bidirectional LSTM; closely followed by a two-branch bidirectional LSTM and GRU architecture.
On Preempting Advanced Persistent Threats Using Probabilistic Graphical Models
Venue: arXiv cs.CR 2019
Year: 2019

This paper presents PULSAR, a framework for pre-empting Advanced Persistent Threats (APTs). PULSAR employs a probabilistic graphical model (specifically a Factor Graph) to infer the time evolution of an attack based on observed security events at runtime. PULSAR (i) learns the statistical significance of patterns of events from past attacks; (ii) composes these patterns into FGs to capture the progression of the attack; and (iii) decides on preemptive actions. PULSAR's accuracy and its performance are evaluated in three experiments at SystemX: (i) a study with a dataset containing 120 successful APTs over the past 10 years (PULSAR accurately identifies 91.7%); (ii) replaying of a set of ten unseen APTs (PULSAR stops 8 out of 10 replayed attacks before system integrity violation, and all ten before data exfiltration); and (iii) a production deployment of PULSAR (during a month-long deployment, PULSAR took an average of one second to make a decision).
Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data
Venue: arXiv cs.LG 2019
Year: 2019

We developed a convolution neural network (CNN) on semi-regular triangulated meshes whose vertices have 6 neighbours. The key blocks of the proposed CNN, including convolution and down-sampling, are directly defined in a vertex domain. By exploiting the ordering property of semi-regular meshes, the convolution is defined on a vertex domain with strong motivation from the spatial definition of classic convolution. Moreover, the down-sampling of a semi-regular mesh embedded in a 3D Euclidean space can achieve a down-sampling rate of 4, 16, 64, etc. We demonstrated the use of this vertex-based graph CNN for the classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) based on 3169 MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the vertex-based graph CNN with that of the spectral graph CNN.
Impact of network delays on Hyperledger Fabric
Venue: arXiv cs.PF 2019
Year: 2019

Blockchain has become one of the most attractive technologies for applications, with a large range of deployments such as production, economy, or banking. Under the hood, Blockchain technology is a type of distributed database that supports untrusted parties. In this paper we focus Hyperledger Fabric, the first blockchain in the market tailored for a private environment, allowing businesses to create a permissioned network. Hyperledger Fabric implements a PBFT consensus in order to maintain a non forking blockchain at the application level. We deployed this framework over an area network between France and Germany in order to evaluate its performance when potentially large network delays are observed. Overall we found that when network delay increases significantly (i.e. up to 3.5 seconds at network layer between two clouds), we observed that the blocks added to our blockchain had up to 134 seconds offset after 100 th block from one cloud to another. Thus by delaying block propagation, we demonstrated that Hyperledger Fabric does not provide sufficient consistency guaranties to be deployed in critical environments. Our work, is the fist to evidence the negative impact of network delays on a PBFT-based blockchain.
Towards Characterizing Divergence in Deep Q-Learning
Venue: arXiv cs.LG 2019
Year: 2019

Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstrapping, off-policy learning, and function approximation. Prior work has demonstrated that together these can lead to divergence in Q-learning algorithms, but the conditions under which divergence occurs are not well-understood. In this note, we give a simple analysis based on a linear approximation to the Q-value updates, which we believe provides insight into divergence under the deadly triad. The central point in our analysis is to consider when the leading order approximation to the deep-Q update is or is not a contraction in the sup norm. Based on this analysis, we develop an algorithm which permits stable deep Q-learning for continuous control without any of the tricks conventionally used (such as target networks, adaptive gradient optimizers, or using multiple Q functions). We demonstrate that our algorithm performs above or near state-of-the-art on standard MuJoCo benchmarks from the OpenAI Gym.
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
Venue: arXiv cs.CL 2019
Year: 2019

The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for continuing dialogues. To further push the end-to-end dialogue model toward real-world scenarios, the seventh Dialog System Technology Challenge (DSTC7) proposed a challenging track based on real chatlog datasets. The competition focuses on dialogue modeling with several advanced characteristics: (1) natural language diversity, (2) capability of precisely selecting a proper response from a large set of candidates or the scenario without any correct answer, and (3) knowledge grounding. This paper introduces recurrent attention pooling networks (RAP-Net), a novel framework for response selection, which can well estimate the relevance between the dialogue contexts and the candidates. The proposed RAP-Net is shown to be effective and can be generalized across different datasets and settings in the DSTC7 experiments.
HELPER: Heterogeneous Efficient Low Power Radio for Enabling Ad Hoc Emergency Public Safety Networks
Venue: arXiv cs.NI 2019
Year: 2019

Natural and man-made disasters have been causing destruction and distress to humanity all over the world. In these scenarios, communication infrastructures are the most affected entities making emergency response operations extremely challenging. This invokes a need to equip the affected people and the emergency responders with the ability to rapidly set up and use independent means of communication. Therefore, in this work, we present a complete end-to-end solution that can connect survivors of a disaster with each other and the authorities using a completely self-sufficient ad hoc network that can be setup rapidly. Accordingly, we develop a Heterogeneous Efficient Low Power Radio (HELPER) that acts as an access point for end-users to connect using custom website application. These HELPERs then coordinate with each other to form a LoRa based ad hoc network. To this end, we propose a novel cross-layer optimized distributed energy-efficient routing (SEEK) algorithm that aims to maximize the network lifetime. The HELPER is prototyped using WiFi enabled Raspberry Pi and LoRa module that is configured to run using Li-ion batteries. We implement the required cross-layer protocol stack along with the SEEK routing algorithm. We have conducted demonstrations to establish the feasibility of exchanging of text messages over the HELPER network, live map updates, ability to send distress messages to authorities. Emergency responders can leverage this technology to remotely monitor the connectivity of the affected area and alert users of imminent dangers. SEEK algorithm was shown to outperform a greedy geographical routing algorithm implemented on HELPER testbed by up to 53 % in terms of network lifetime and up to 28 % in terms of throughput. Overall, we hope this technology will become instrumental in improving the efficiency and effectiveness of public safety activities.
Towards a Forensic Event Ontology to Assist Video Surveillance-based Vandalism Detection
Venue: arXiv cs.AI 2019
Year: 2019

The detection and representation of events is a critical element in automated surveillance systems. We present here an ontology for representing complex semantic events to assist video surveillance-based vandalism detection. The ontology contains the definition of a rich and articulated event vocabulary that is aimed at aiding forensic analysis to objectively identify and represent complex events. Our ontology has then been applied in the context of London Riots, which took place in 2011. We report also on the experiments conducted to support the classification of complex criminal events from video data.
Subgraph Networks with Application to Structural Feature Space Expansion
Venue: arXiv cs.SI 2019
Year: 2019

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
Recent advances in conversational NLP : Towards the standardization of Chatbot building
Venue: arXiv cs.CL 2019
Year: 2019

Dialogue systems have become recently essential in our life. Their use is getting more and more fluid and easy throughout the time. This boils down to the improvements made in NLP and AI fields. In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. We end up with a discussion that compares all the techniques and analyzes the strengths and weaknesses of each. Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building.